Computer Science > Computation and Language

Title:Progressive Memory Banks for Incremental Domain Adaptation

Abstract: This paper addresses the problem of incremental domain adaptation (IDA). We
assume each domain comes one after another, and that we could only access data
in the current domain. The goal of IDA is to build a unified model performing
well on all the domains that we have encountered. We propose to augment a
recurrent neural network (RNN) with a directly parameterized memory bank, which
is retrieved by an attention mechanism at each step of RNN transition. The
memory bank provides a natural way of IDA: when adapting our model to a new
domain, we progressively add new slots to the memory bank, which increases the
number of parameters, and thus the model capacity. We learn the new memory
slots and fine-tune existing parameters by back-propagation. Experimental
results show that our approach achieves significantly better performance than
fine-tuning alone, which suffers from the catastrophic forgetting problem.
Compared with expanding hidden states, our approach is more robust for old
domains, shown by both empirical and theoretical results. Our model also
outperforms previous work of IDA including elastic weight consolidation (EWC)
and the progressive neural network.